| Summary: | 碩士 === 國立中正大學 === 電機工程研究所 === 105 === In recent years, with the rise of 3D TV, the film, 3D technology by the academic community and the industry's attention, more and more people agree that the future will be 3D stereo video technology rapid growth stage. However, due to the difficulty of building a 3D viewing environment and solving various 3D quality factors such as visual discomfort, 3D-VQA’s (3D Video Quality Assessment) research is less, so the assessment of 3D video quality requirements is urgently needed.
This paper evaluates the 3D video quality of the color plus depth format. The depth of the current estimation algorithm, the depth of the assessment of the quality of 3D stereo image quality, such as when the depth edge and color edge can not be aligned, or the depth map estimate error, it will cause 3D stereoscopic images When the foreground material concave to the screen or the background of the protrusion of the adverse effects of the screen, so to assess the depth of the 3D subjective quality score is our main purpose of this paper.
In this paper, several kinds of depth estimation methods are used, we obtain the left and right eye stereoscopic images by using DIBR technique. Then, the subjective scores of stereo images are obtained by subjective scoring method. Different subjective scores are obtained by different depth estimation methods. And then use the four visual feature as input, such as: motion vector, color and depth edge map ... and so on, and then use the deep learning about convolution neural network architecture to do the subjective assessment of the scores of training and prediction. The deep learning part uses the convolution neural network because the convolution neural network is a learning architecture composed of one or more convolution layers and a fully connected layer, which is excellent for image processing or classification. The overall structure of this paper is a non-reference objective quality assessment, which means that the 3D video quality predicted by the deep learning architecture can be close to the subjective quality of the human eye in order to judge whether the depth map of the trained depth estimation method is good or bad Advantages and disadvantages of training depth estimation method.
The experimental results show that the proposed scheme is more than 70% on the experimental one, that is, the depth map of the trained depth estimation method. In experiment 2, the accuracy of the untrained depth estimation method can be as high as 80% to 90%. But also found some problems, subjective scores of the sample too concentrated and lead to less sample interval judgment error.
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